CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Spatial variations and development of land use regression models of oxidative potential in ten European study areas
Authors
A. Jedynska Hoek, G. Wang, M. Yang, A. Eeftens, M. Cyrys, J. Keuken, M. Ampe, C. Beelen, R. Cesaroni, G. Forastiere, F. Cirach, M. de Hoogh, K. De Nazelle, A. Nystad, W. Akhlaghi, H.M. Declercq, C. Stempfelet, M. Eriksen, K.T. Dimakopoulou, K. Lanki, T. Meliefste, K. Nieuwenhuijsen, M. Yli-Tuomi, T. Raaschou-Nielsen, O. Janssen, N.A.H. Brunekreef, B. Kooter, I.M.
Publication date
1 January 2017
Publisher
Abstract
Oxidative potential (OP) has been suggested as a health-relevant measure of air pollution. Little information is available about OP spatial variation and the possibility to model its spatial variability. Our aim was to measure the spatial variation of OP within and between 10 European study areas. The second aim was to develop land use regression (LUR) models to explain the measured spatial variation. OP was determined with the dithiothreitol (DTT) assay in ten European study areas. DTT of PM2.5 was measured at 16–40 sites per study area, divided over street, urban and regional background sites. Three two-week samples were taken per site in a one-year period in three different seasons. We developed study-area specific LUR models and a LUR model for all study areas combined to explain the spatial variation of OP. Significant contrasts between study areas in OP were found. OP DTT levels were highest in southern Europe. DTT levels at street sites were on average 1.10 times higher than at urban background locations. In 5 of the 10 study areas LUR models could be developed with a median R2 of 33%. A combined study area model explained 30% of the measured spatial variability. Overall, LUR models did not explain spatial variation well, possibly due to low levels of OP DTT and a lack of specific predictor variables. © 2016 Elsevier Lt
Similar works
Full text
Available Versions
Pergamos : Unified Institutional Repository / Digital Library Platform of the National and Kapodistrian University of Athens
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:lib.uoa.gr:uoadl:3057869
Last time updated on 10/02/2023